import argparse
import json

from tqdm import tqdm





def 			lowercase(	)		->	str:







 '''simple docstring'''
 UpperCamelCase			  =      argparse.ArgumentParser()

 # Required parameters
 parser.add_argument(
     """--src_path"""							,					type=UpperCamelCase_							,					default="""biencoder-nq-dev.json"""							,					help="""Path to raw DPR training data"""							,					)
 parser.add_argument(
     """--evaluation_set"""							,					type=UpperCamelCase_							,					help="""where to store parsed evaluation_set file"""							,					)
 parser.add_argument(
     """--gold_data_path"""							,					type=UpperCamelCase_							,					help="""where to store parsed gold_data_path file"""							,					)
 UpperCamelCase			  =      parser.parse_args()

 with open(args.src_path							,					"""r"""       ) as src_file, open(args.evaluation_set							,					"""w"""       ) as eval_file, open(
     args.gold_data_path							,					"""w"""       ) as gold_file:
  UpperCamelCase			  =      json.load(UpperCamelCase_       )
  for dpr_record in tqdm(UpperCamelCase_       ):
   UpperCamelCase			  =      dpr_record["""question"""]
   UpperCamelCase			  =      [context["""title"""] for context in dpr_record["""positive_ctxs"""]]
   eval_file.write(question + """\n"""       )
   gold_file.write("""\t""".join(UpperCamelCase_       ) + """\n"""       )


if __name__ == "__main__":
     main()

import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      MobileBertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      MobileBertForPreTraining(UpperCamelCase_       )
 # Load weights from tf checkpoint
 UpperCamelCase			  =      load_tf_weights_in_mobilebert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--mobilebert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained MobileBERT model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
